Our Services
AgentLab is a data visualization, data engineering and ML team with a strong Linked Data and Systems Engineering background.
Ontology engineering and Linked Data modeling
We integrate your system into a bigger ecosystem with the help of Industrial Common Information Models (IEC CIM, ISO 15926, OASIS OSLC, DataSpaces and similar standards), public ontologies (W3C DCAT/SSN/PROV, Schema.org, BFO, SAREF) and vocabularies (EuroVoc, AGROVOC).
We help you to harness the power of public Knowledge Graphs like WikiData and DBPedia.
We model your data as light-weight RDF Schema (RDFS) with SHACL Shapes constraints.
We have an experience in different domain areas of data modeling: technical systems & IIoT; data engineering & machine learning (datasets, workflows, quality, ML methods, models); PLM, ALM & Engineering data integration (OASIS OSLC), methods and practices (OMG Essence).
Data virtualization with Linked Data Virtual Graphs
We virtualize your heterogenous storages (relational, NoSQL, BigData, triplestores) and represent it as a single queryable SPARQL Endpoint, handling all-needed data and metadata as a hole RDFS graph.
Web Interfaces for Linked Data browsing and editing
We build schema-based domain-specific UIs with forms, trees, tables and tree-tables to view, search, asses and edit your RDF data.
Domain-specific Linked Data visualization and editing with diagrams and data charts
We help you visualize RDF graphs as domain-specific interactive diagrams (simple node-edge graphs, class diagrams, ontology diagrams). We could make diagrams editable.
We help you visualize time-series RDF data in interactive charts.
Data engineering & Data quality based Linked Data metadata
We enhance your data description with semantic Linked Data metadata. If you'd like it could be as complex as Semantic Data Lake.
We develop setup Spark-based data preparation routines and represent data provenance in W3C PROV.
We develop setup a data quality services to monitor data quality and handle data quality data and metadata in W3C DQV-compliant manner.
Automated Machine Learning (AutoML) based on Linked Data metadata
We develop and deploy an AutoML infrastructure for your BigData, enhanced with Linked Data metadata.